Enterprises can no longer afford the liability of unverified AI. If you can’t prove its provenance, you can’t trust its output. That isn’t rhetoric, regulators and large institutions now expect verifiable lineage, documented controls, and auditability across the AI lifecycle (e.g., EU AI Act; NIST AI RMF).

AI is evolving from predictive tools into federations of autonomous agents that negotiate contracts, manage supply chains, execute trades, and operate critical infrastructure. When AI can act in the real world, “unverifiable” is not an option.

The Trust Gap in AI

• Provenance & Lineage. You need cryptographic provenance and end-to-end lineage for models, data, and outputs to reduce legal and operational risk. U.S. NTIA explicitly calls for provenance and authentication so organizations can identify sources, report incidents, and assign responsibility.

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• Accountability. Emerging AgentFacts (Know-Your-Agent) and verifiable credentials (VCs) let agents carry signed identities and action logs, so enterprises can prove who or what acted. (AgentFacts is an emerging standard; pair it with VCs for cryptographic attestations.)

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• Transparency. AI Bill of Materials (AI-BOM) and chain-of-custody logs give auditable, supply-chain-style traceability, an approach echoed in policy and research discussions on AI accountability and data provenance.

Our Mission: Make Trust Verifiable

We enable organizations to deploy powerful AI with confidence that every decision is explainable, auditable, and regulator-ready.

1) Accountability

• Immutable audit trails for every AI action.

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• AgentFacts + Verifiable Credentials to cryptographically sign agent identity and actions (who built it, updates, policies it follows).

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• Chains of custody for data/models/outputs to preserve responsibility end-to-end. (Policy bodies and research emphasize this for accountability and incident response.)

2) Governance

• Security & Access Controls. Enforce RBAC/ABAC and least-privilege across AI services in line with NIST SP 800-53 and ISO/IEC 27001 so only authorized users/agents can invoke models, tools, or data.

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• Policy Guardrails. Block disallowed topics, toxic content, data exfiltration, and jailbreaks; log and justify model decisions; and apply data loss prevention (DLP). Major platforms now ship configurable guardrails and safety filters that sit between users/agents and models (e.g., AWS Bedrock Guardrails, Google Vertex AI safety filters, Microsoft Purview DLP for AI).

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• Operational Compliance. Built-in mappings to EU AI Act obligations (risk classification, technical documentation) and NIST AI RMF functions (Map–Measure–Manage–Govern).

3) Transparency

• AI-BOM (components, datasets, fine-tunes, prompts, tools) + chain-of-custody for complete dependency tracing.

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• Watermarking & Content Provenance. Embed C2PA/Content Credentials and use SynthID-style watermarking so downstream users can verify whether content is synthetic or altered.

The AI Trust Crisis is Ending

• End-to-end coverage from training to deployment to audit.

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• Zero-trust interoperability for multi-agent systems (verifiable agent-to-agent policies).

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• Regulator-ready reporting for EU AI Act, ISO/IEC 42001, NIST AI RMF, and U.S. federal guidance (OMB M-24-10).

Who We Are

Seasoned technologists with decades building enterprise-grade systems in regulated environments. We align to EU AI Act, NIST AI RMF, ISO/IEC 27001/42001, and banking SR 11-7 practices, so trust isn’t a promise; it’s a property your auditors can verify.

Trust your AI. Govern your models. Prove your decisions.

📩 contact@krnl.xyz